Abstract
In recent years, many graph-based methods have been introduced to detect saliency. These methods represent image regions and their similarity as vertices and edges in a graph. However, since they only represent pairwise relations between vertices, they give an incomplete representation of the relationships between regions. In this work, we propose a hypergraph based optimization framework for salient object detection to include not only the pairwise but also the higher-order relations among two or more vertices. In this framework, besides the relations among vertices, both the foreground and the background queries are explicitly exploited to uniformly highlight the salient objects and suppress the background. Furthermore, a probabilistic hypergraph is constructed based on local spatial correlation, global spatial correlation, and color correlation to represent the relations among vertices from different views. Extensive experiments demonstrate the effectiveness of the proposed method.
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Acknowledgments
This work was supported by the National Natural Science Fund of China (Grant numbers 61233011, 61374006, 61473086, 61703100); Major Program of National Natural Science Foundation of China (Grant number 11190015); Natural Science Foundation of Jiangsu (Grant number BK20131300, BK20170692); the Innovation Fund of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education (Nanjing University of Science and Technology, Grant number JYB201601); the Innovation Fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering (Southeast University, Grant number MCCSE2017B01); and the Fundamental Research Funds for the Central Universities (2242016k30009).
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Zhang, J., Fang, S., Ehinger, K.A., Guo, W., Yang, W., Wei, H. (2017). Probabilistic Hypergraph Optimization for Salient Object Detection. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_32
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DOI: https://doi.org/10.1007/978-3-319-67777-4_32
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